Dana Segev (EE, Technion)
Tuesday, 24.1.2012, 11:30
Audio denoising is a long studied problem, with numerous algorithms and a
wide accumulated knowledge. Considering non-stationary noise (like noise in
a cocktail party environment) and strong, this task becomes very difficult
to handle. This paper considers such audio denoising problems, where the
audio track is accompanied by a video. Furthermore, the disturbing source
is generally not visible in the field of view, and its nature is unknown.
Overcoming such unknown noise is different than source separation, where
the disturbing sources or their priors are at least partially accessible.
We demonstrate the core-ability to use the video information to better
filter the audio. The approach we take in this work is an example-based
one, assuming that we have at our disposal a relatively good-quality movie
(video+audio) to train on. The noise removal itself is done by processing
short temporal segments of video-and-audio, seeking relevant examples from
the training set. The video information eliminates non- relevant training
examples and improves the algorithm performance. We demonstrate this
approach on two different types of signals. In all the experiments, a very
significant denoising is achieved, also in cases where audio-only
processing methods fail.
MSc thesis under the supervision of Michael Elad and Yoav Schechner.